Factors Associated with Complicated Grief Following a Railway Tragedy
Bibliographic record
Abstract
On July 6, 2013, a train with 72 crude oil tank cars derailed in the heart of Lac-Mégantic, a small municipality of 6,000 inhabitants located in Québec (Canada). This tragedy killed 47 people. Technological disasters are rarely studied in bereavement research, and train derailments even less. The goal of this article is to increase our understanding of the bereavement consequences of technological disasters. Specifically, we aim to identify the factors that lead to the experience complicated grief and distinguish from the protective factors. A representative population-based survey was conducted among 268 bereaved people, three and a half years after the train accident. Of these, 71 people (26.5%) experienced complicated grief. People with complicated grief (CG) differ significantly from those without CG in terms of psychological health, perception of physical health, alcohol use and medication, as well as social and professional relationships. Hierarchical logistic regression analysis identified four predictive factors for CG: level of exposure to the disaster, having a negative perception of the event, as well as having a paid job and low-income increase the risk of CG. The importance of having health and social practitioners pay attention to these factors of CG are discussed along with future directions for research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".